ACT Regional Centre


Upcoming events


Annual General Meeting


When: 5.30 pm-8.00 pm, Tuesday 6 December

Where: F102, Forestry Building, ANU (Building 48)

Agenda:
  • 5:30 - 6:00pm: Drinks and nibbles
  • 6:00 - 7:00pm: Student presentations
  • 7:00 - 8:00pm: AGM and food (pizza and salads)

RSVP: Clem.Davis@anu.edu.au



Student presentations


Characterising an Indigenous Seasonal Calendar: Complex Definitions and Observed Weather
Zac Hatfield Dodds

Indigenous Seasons are defined in terms of environmental conditions - meteorological and ecological - and carry significant information about the local climate. Unfortunately, they are often presented simply as an alternative to DJF (etc) in dividing up calendar days. For my Honours, I traveled to northern Australia and spoke to Arnhem Land elders about the structure and definitions of their seasonal calendars, focusing on the more tractable meteorological aspects. Following qualitative analysis of the definitions, I constructed quantitative definitions of the 'weather seasons' based on BOM observations, and used this new data to analyse the timing, patterns, and inter-annual variability of the seasons.


The role of the sea breeze in severe storm formation in Brisbane
Joss Kirk

Due to a combination of climatic conditions, topography, and instability-producing mesoscale processes, Brisbane has earned a reputation as thunderstorm hot-spot. One of the key mesoscale processes that drives atmospheric instability for Brisbane and southeast Queensland is the sea breeze. Case studies and volumetric radar analyses have shown that the moisture and shear brought by the sea breeze can engender better storm organisation and longevity. This analysis presents a new algorithm for detecting occurrences of the sea breeze throughout the observational record, allowing for a consideration of the effect of the sea breeze on storm formation for Brisbane. Instead of considering volumetric radar data as has been done previously, this analysis had a predictive focus, focusing on vertical atmospheric profiles and key indicators of instability such as convective available potential energy (CAPE) and convective inhibition (CIN). The instability engendered by the sea breeze was shown to allow severe thunderstorm development despite low levels of CAPE and high levels of CIN, a relationship that was generally stronger in the months of January and February than in the other major storm months of October, November and December. For these other months, synoptic scale processes such as surface pressure troughs were shown to play a more important role than they did during January and February.


Application of a Hidden Markov Model to remotely sensed snow cover measurements of a Himalayan basin
Sean Minhui Tashi Chua

Remote sensing is used to monitor snow cover in areas without sufficient in-situ observations such as the Himalayas. However, commonly used remotely sensed snow cover products such as those provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) have reduced accuracy in mountainous areas due to the negative influence of atmospheric effects and terrain in these regions. This thesis investigates the ability of a Hidden Markov model to limit erroneous misclassifications and reduce unrealistic estimates of snow cover. A Hidden Markov model is able to utilise a series of input observations, MODIS snow cover products in this case, and use these to calculate the probability of them representing the ground state. These probabilities can then be used to model the most likely series of states, this effectively provides a dynamic filter than can mitigate the problems faced when remotely sensing snow in mountainous areas. This approach was evaluated by applying a Hidden Markov model to MODIS snow cover measurements of a sub-basin in Eastern Nepal. The Hidden Markov model resulted in 16% and 9% increases in agreement with corresponding evaluation data than when compared to the two equivalent MODIS snow cover products that provided input data. The results also highlighted the ability of this approach to reduce data noise. Comparisons of snow covered area over time showed improved consistency in measurements when comparing the model output to both the daily and 8-day composite MODIS snow cover products. The ability of a Hidden Markov model to employ a dynamic filter that can utilise entire sequences of observations could offer improved accuracy when compared to other time-series filtering methods.